Acoustic Timbre Recognition

Author(s):  
Daniel Pressnitzer ◽  
Trevor Agus ◽  
Clara Suied
Keyword(s):  
2015 ◽  
Vol 26 (6) ◽  
pp. 2483-2496 ◽  
Author(s):  
F. Occelli ◽  
C. Suied ◽  
D. Pressnitzer ◽  
J.-M. Edeline ◽  
B. Gourévitch

2021 ◽  
pp. 1-12
Author(s):  
Stephanie L. Fowler ◽  
Hannah Calhoun ◽  
Andrea D. Warner-Czyz

Purpose Adult cochlear implant (CI) users rate music as one of the most important auditory stimuli, second to speech perception. However, few studies simultaneously examine music perception and speech-in-noise perception in adult CI recipients. This study explores the effect of auditory status on music perception and speech-in-noise perception recognition in noise as well as the relationship among music engagement, music perception, and speech-in-noise perception. Method Participants include 10 adults with typical hearing (TH) and 10 adults with long-term CI use. All participants completed the Music-Related Quality of Life Questionnaire, which assesses subjective music experiences and their importance; the Pitch Direction Discrimination, Familiar Melody Recognition, and Timbre Recognition subtests of the Clinical Assessment of Music Perception for Cochlear Implants; the Unfamiliar Melody Recognition subtest of the Profile of Music Perception Skills; and the Bamford–Kowal–Bench Speech-in-Noise Test . Results The TH group significantly outperformed the CI group for speech-in-noise perception and on all four music perception tasks. The CI group exhibited not only significantly poorer mean scores but also greater variability in performance compared to the TH group. Only Familiar Melody Recognition and Unfamiliar Melody Recognition subtests significantly correlated with speech-in-noise scores. Conclusions Patients and professionals should not assume speech perception and music perception in adult CI users derive from the same auditory or cognitive foundations. The lack of significant relationships among music engagement, music perception, and speech-in-noise perception scores in adult CI users suggests this population enjoys music despite poor and variable performance in discrete music tasks.


2019 ◽  
Vol 29 (51) ◽  
pp. 1907151 ◽  
Author(s):  
Caihao Deng ◽  
Peixiong Gao ◽  
Linfeng Lan ◽  
Penghui He ◽  
Xin Zhao ◽  
...  

2020 ◽  
Vol 12 (51) ◽  
pp. 57352-57361
Author(s):  
Tingting Yang ◽  
Wen Wang ◽  
Yuehua Huang ◽  
Xin Jiang ◽  
Xuanliang Zhao

2002 ◽  
Vol 13 (03) ◽  
pp. 132-145 ◽  
Author(s):  
Kate Gfeller ◽  
Shelley Witt ◽  
Mary Adamek ◽  
Maureen Mehr ◽  
Jenny Rogers ◽  
...  

The purpose of this study was to compare the effect of structured training on recognition and appraisal of the timbre (tone quality) of musical instruments by postlingually deafened cochlear implant recipients. Twenty-four implant users (Nucleus C124M) were randomly assigned to a control or a training group. The control group experienced only incidental exposure to music in their usual daily routine. The training group participated in 12 weeks of training delivered via a laptop computer in which they were introduced to excerpts of musical instruments representing three frequency ranges and four instrumental families. Those implant recipients assigned to the training group showed significant improvement in timbre recognition (p < .0001) and timbre appraisal (p < .02) compared to the control group. Correlations between timbre measures and speech perception measures are discussed.


2021 ◽  
Vol 10 (1) ◽  
Author(s):  
Ellen Jannereth ◽  
Lisa Esch

A sound’s unique timbre is based on the various harmonic frequencies present within its waveform. Through Fast Fourier Transform software, waveforms can be easily decomposed into their component frequencies and a spectral analysis of frequency can be conducted as a method of quantitatively describing timbral characteristics of a sound. In this investigation, the range of frequencies present in a spectrum as well as the average intensity of the first 10 overtones in a sound will be used to classify the timbres of various instruments relative to one another. This will be done by generating a Range-Intensity graph of harmonic frequencies present in sound samples of each instrument. The results of this investigation reveal that it is not only possible to quantitatively analyze instrumental timbre by generating and mapping out the harmonic frequency data of a specific sound, but that such a quantitative analysis is also incredibly useful. Unlike the traditional, qualitative method of describing timbre, a quantitative analysis would allow for timbral qualities to be transformed into information that can be understood by computers. Today, timbral classification and the decomposition of waveforms has many applications in science and sound engineering. By refining methods for quantitative timbral analysis, it becomes possible to further enhance timbre recognition software and apply such methods to a wider range of technological developments.


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